Triple
T15280537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alagna Valsesia |
E365255
|
entity |
| Predicate | hasSkiAreaAltitudeRange |
P117028
|
FINISHED |
| Object | approximately 1200–3275 metres |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: approximately 1200–3275 metres | Statement: [Alagna Valsesia, hasSkiAreaAltitudeRange, approximately 1200–3275 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkiAreaAltitudeRange Context triple: [Alagna Valsesia, hasSkiAreaAltitudeRange, approximately 1200–3275 metres]
-
A.
skiAreaAltitudeRange_m
chosen
Indicates the range of altitudes, in meters, over which a ski area extends.
-
B.
hasSkiAreaBaseElevation
Indicates the base elevation at which a ski area is situated.
-
C.
hasSkiAreaVerticalDrop
Indicates the vertical distance in elevation between the highest and lowest points of a ski area.
-
D.
hasSkiRunsLength_km
Indicates the total length, in kilometers, of the ski runs associated with an entity.
-
E.
hasSkiAreaSide
Indicates that something is located on or associated with a particular side or slope of a ski area.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e504d8c8190ad6c565a31d1a9bd |
completed | April 15, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.